In general, semiconductor chips are manufactured several at a time on wafers. These wafers are in turn grouped together to form so-called lots, in which a multiplicity of individual wafers are grouped together for further handling of the wafer object data.
In the manufacture of wafers with highly integrated semiconductor chips, the ever-increasing miniaturization of the structures on the semiconductor chip is responsible in particular for imposing ever greater requirements on the production installations and manufacturing processes used for the manufacture of the semiconductor chips. The stability and reproducibility both of the production installations and of the manufacturing processes decisively influence the yield and productivity during semiconductor chip production. Even small deviations from a prescribed form of behavior of a chip production installation during production can lead to considerable worsening of the yield (i.e. a considerable increase in the defect rate of the semiconductor chips manufactured).
For quality management (i.e. to ensure the quality of the manufacturing process and the quality of the wafers) the wafers must be subjected to test measurements once processing of them has been completed. In order to monitor and assess the manufacturing process completely, it would in fact be necessary to test each individual wafer that has been produced by means of the manufacturing process and subsequently assess the quality of the wafer for a complete and exact determination of the product quality of the wafers of a lot. This is not possible, however, on account of the time-intensive and cost-intensive test measurements for determining the quality of the wafers.
For quality management, a “Statistical Process Control” (SPC) method is often used. According to this method, wafers selected as random samples are tested for their quality by means of test measurements and, on the basis of the results of the investigations of the random samples, the product quality of entire lots of a manufacturing process is determined. The random samples are in this case taken randomly from the wafers of a lot. By means of this SPC method, the average quality of the wafers of a lot or of a manufacturing process is determined.
In quality management it is necessary not only to determine the average quality of the wafers of a lot but also to segregate wafers which are of a low quality and consequently do not meet a prescribed quality limit. These wafers are then sent for special treatment, i.e. for a special measurement, possible re-working or segregation. For this purpose it is necessary to determine those wafers of a lot that are at risk of not meeting the prescribed quality limit because faults occurred during their processing in the manufacturing process. For this purpose, the so-called “Fault Detection and Classification” (FDC) method is often used. By means of this method, process faults, i.e. faults which have occurred in the manufacturing process, are registered. The corresponding defects are also referred to in the application as FDC defects.
The detection of wafers which do not meet the quality limit should also be performed as early as possible and as reliably as possible in the manufacturing process, in order to notice a possible problem in the manufacture as early as possible. One possibility for determining the wafers which possibly do not meet the quality limit (bad wafers) is the use of so-called process data. These process data are values of process parameters which are continuously recorded during the manufacturing process. Examples of process data are, for example, the pressure or the temperature in a process chamber during a process step.
According to the prior art, for detecting the bad wafers, an upper and/or lower limit value or threshold value is stored for each process parameter. The value of the process parameter (process datum) must not go above or below this limit value in the manufacturing process. According to the prior art, however, there is the problem that the number of these limit values for a wafer production installation very quickly reaches a value which is no longer manageable, since a product which is to be monitored can be manufactured by means of various operations, machines, formulations, etc. The operations, machines, formulations, etc. are referred to hereafter as hierarchical levels (i.e. a hierarchical level is for example the set of all the formulations occurring). Consequently, the number of limit values to be stored results from the number of combinations of hierarchical levels occurring in the manufacture of the wafers (i.e. the number of products produced multiplied by the number of process steps required for the production multiplied by the number of formulations per product multiplied by the number of possible machines). In a typical case with 300 products, with 20 process steps, with 10 formulations per product and with 20 possible machines, the number of limit values to be stored amounts to 1,200,000. This number is so high that the corresponding set of limit values is no longer manageable or maintainable. Consequently, monitoring of these limit values is not practically possible.
Document D1 (US 2002/0055194 A1) discloses a method for the quality monitoring of a production line, in which a trend in the quality characteristics of the production line or fluctuations are detected at an early stage.
U.S. Pat. No. 5,726,920 discloses a final-wafer-sorting (FWS) testing facility, in which raw output data that are output by means of FWS test stations are augmented with additional differentiating data to produce thereby differentiable output data which can be sorted according to a number of criteria.
C. Schneider et al., IEEE/SEMI Advanced Semiconductor Manufacturing Conference, April 2001, pages 33 to 40 discloses an automatic control of the “Critical Dimension (CD)” in a photolithographic method. For this purpose, a system which stores in a database historical settings of production tools for every combination of product, reticle, mask layer and mold combination is presented.
The invention is based on the problem of making the monitoring of process parameters of a manufacturing process simpler and more effective.